For volume image reconstruction, an iterative algorithm has been developed by various groups and includes a total variation (TV) minimization iterative reconstruction algorithm. Iterative reconstruction (IR) additionally involves Algebraic Reconstruction Technique (ART), Simultaneous Algebraic Reconstruction Technique (SART) or Ordered-subset Simultaneous Algebraic Reconstruction Technique (OS-SART).
In X-ray computed tomography (CT), iterative reconstruction (IR) has gained some attention to improve certain aspects of image quality over conventional filtered backprojection (FBP). IR is based on a forward model that accurately estimates the attenuation line integrals, while keeping computational complexity manageable. On the other hand, FBP is based upon reconstruction kernels.
Prior art has attempted to improve spatial resolution in both IR and FBP techniques. In conventional FBP techniques, one way to improve spatial resolution is to apply sharp convolution kernels with high-frequency boost (FBP-HR) to undo spatial blurring factors in the imaging system, such as finite focal spot size, finite detector cell size, detector cross talk and azimuthal blur. Although IR does not have the notion of reconstruction kernels, IR still can improve the image resolution and image noise.
Prior art IR techniques enhanced the spatial resolution with certain noise compensation means. One prior art IR technique has utilized an enlarged voxel footprint in the forward model, combined with a band suppression filter designed to eliminate any undesirable over- or under-shoot artifacts that may arise from the use of the enlarged voxels. Another prior art approach has used libraries of point-spread functions to model the spatially varying voxel footprint.
Despite the above prior art efforts, a trade off still exists between the noise suppressions and the spatial resolution improvement among the iterative reconstruction techniques.